{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf \n",
    "from tensorflow import keras\n",
    "from  tensorflow.keras import layers\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"toy_resnet\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "img (InputLayer)                [(None, 32, 32, 3)]  0                                            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d (Conv2D)                 (None, 30, 30, 32)   896         img[0][0]                        \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_1 (Conv2D)               (None, 28, 28, 64)   18496       conv2d[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d (MaxPooling2D)    (None, 9, 9, 64)     0           conv2d_1[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_2 (Conv2D)               (None, 9, 9, 64)     36928       max_pooling2d[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_3 (Conv2D)               (None, 9, 9, 64)     36928       conv2d_2[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "add (Add)                       (None, 9, 9, 64)     0           conv2d_3[0][0]                   \n",
      "                                                                 max_pooling2d[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_4 (Conv2D)               (None, 9, 9, 64)     36928       add[0][0]                        \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_5 (Conv2D)               (None, 9, 9, 64)     36928       conv2d_4[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "add_1 (Add)                     (None, 9, 9, 64)     0           conv2d_5[0][0]                   \n",
      "                                                                 add[0][0]                        \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_6 (Conv2D)               (None, 7, 7, 64)     36928       add_1[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "global_average_pooling2d (Globa (None, 64)           0           conv2d_6[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "dense (Dense)                   (None, 256)          16640       global_average_pooling2d[0][0]   \n",
      "__________________________________________________________________________________________________\n",
      "dropout (Dropout)               (None, 256)          0           dense[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "dense_1 (Dense)                 (None, 10)           2570        dropout[0][0]                    \n",
      "==================================================================================================\n",
      "Total params: 223,242\n",
      "Trainable params: 223,242\n",
      "Non-trainable params: 0\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "#使用ResNet模型\n",
    "#使用CIFAR10建立一个Demo ResNet模型\n",
    "inputs=keras.Input(shape=(32,32,3),name='img')\n",
    "x=layers.Conv2D(32,3,activation=\"relu\")(inputs)\n",
    "x=layers.Conv2D(64,3,activation='relu')(x)\n",
    "block_1_output=layers.MaxPooling2D(3)(x)\n",
    "\n",
    "x=layers.Conv2D(64,3,activation=\"relu\",padding='same')(block_1_output)\n",
    "x=layers.Conv2D(64,3,activation=\"relu\",padding='same')(x)\n",
    "block_2_output=layers.add([x,block_1_output])\n",
    "\n",
    "x=layers.Conv2D(64,3,activation=\"relu\",padding='same')(block_2_output)\n",
    "x=layers.Conv2D(64,3,activation=\"relu\",padding='same')(x)\n",
    "block_3_output=layers.add([x,block_2_output])\n",
    "\n",
    "x=layers.Conv2D(64,3,activation='relu')(block_3_output)\n",
    "x=layers.GlobalAveragePooling2D()(x)\n",
    "x=layers.Dense(256,activation='relu')(x)\n",
    "x=layers.Dropout(0.5)(x)\n",
    "outputs = layers.Dense(10)(x)\n",
    "\n",
    "model = keras.Model(inputs, outputs, name=\"toy_resnet\")\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "keras.utils.plot_model(model, \"resnet-cifar.png\", show_shapes=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "#加载模型\n",
    "(x_train,y_train),(x_test,y_test)=keras.datasets.cifar10.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "#归一化处理\n",
    "x_train=x_train.astype('float32')/255\n",
    "x_test=x_test.astype('float32')/255\n",
    "\n",
    "#向量化处理\n",
    "y_train=keras.utils.to_categorical(y_train,10)\n",
    "y_test=keras.utils.to_categorical(y_test,10)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "#编译模型\n",
    "model.compile(\n",
    "optimizer=keras.optimizers.RMSprop(1e-3),\n",
    "loss=keras.losses.CategoricalCrossentropy(from_logits=True),\n",
    "metrics=['acc'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 800 samples, validate on 200 samples\n",
      "Epoch 1/100\n",
      "800/800 [==============================] - 2s 3ms/sample - loss: 2.3041 - acc: 0.0887 - val_loss: 2.2957 - val_acc: 0.1100\n",
      "Epoch 2/100\n",
      "800/800 [==============================] - 1s 2ms/sample - loss: 2.2870 - acc: 0.1300 - val_loss: 2.2251 - val_acc: 0.1400\n",
      "Epoch 3/100\n",
      "800/800 [==============================] - 1s 2ms/sample - loss: 2.2859 - acc: 0.1562 - val_loss: 2.1729 - val_acc: 0.1450\n",
      "Epoch 4/100\n",
      "800/800 [==============================] - 1s 2ms/sample - loss: 2.1926 - acc: 0.1475 - val_loss: 2.2448 - val_acc: 0.1500\n",
      "Epoch 5/100\n",
      "800/800 [==============================] - 1s 2ms/sample - loss: 2.1474 - acc: 0.1737 - val_loss: 2.1532 - val_acc: 0.1700\n",
      "Epoch 6/100\n",
      "800/800 [==============================] - 1s 2ms/sample - loss: 2.1563 - acc: 0.1975 - val_loss: 2.1800 - val_acc: 0.1800\n",
      "Epoch 7/100\n",
      "800/800 [==============================] - 1s 2ms/sample - loss: 2.1146 - acc: 0.1887 - val_loss: 2.0867 - val_acc: 0.2100\n",
      "Epoch 8/100\n",
      "800/800 [==============================] - 1s 2ms/sample - loss: 2.0784 - acc: 0.2225 - val_loss: 2.0078 - val_acc: 0.2350\n",
      "Epoch 9/100\n",
      "800/800 [==============================] - 1s 2ms/sample - loss: 2.0700 - acc: 0.2150 - val_loss: 2.0264 - val_acc: 0.2050\n",
      "Epoch 10/100\n",
      "800/800 [==============================] - 1s 2ms/sample - loss: 2.0691 - acc: 0.1937 - val_loss: 2.0890 - val_acc: 0.1950\n",
      "Epoch 11/100\n",
      "800/800 [==============================] - 1s 2ms/sample - loss: 2.0462 - acc: 0.2075 - val_loss: 2.2303 - val_acc: 0.2200\n",
      "Epoch 12/100\n",
      "800/800 [==============================] - 1s 2ms/sample - loss: 2.0240 - acc: 0.1975 - val_loss: 1.9551 - val_acc: 0.2500\n",
      "Epoch 13/100\n",
      "800/800 [==============================] - 1s 2ms/sample - loss: 1.9335 - acc: 0.2488 - val_loss: 2.8687 - val_acc: 0.1350\n",
      "Epoch 14/100\n",
      "800/800 [==============================] - 1s 2ms/sample - loss: 1.9941 - acc: 0.2587 - val_loss: 1.9146 - val_acc: 0.2000\n",
      "Epoch 15/100\n",
      "800/800 [==============================] - 1s 2ms/sample - loss: 1.9385 - acc: 0.2475 - val_loss: 2.1770 - val_acc: 0.1700\n",
      "Epoch 16/100\n",
      "800/800 [==============================] - 1s 2ms/sample - loss: 1.9544 - acc: 0.2463 - val_loss: 1.8787 - val_acc: 0.2450\n",
      "Epoch 17/100\n",
      "800/800 [==============================] - 1s 2ms/sample - loss: 1.8859 - acc: 0.2650 - val_loss: 2.0983 - val_acc: 0.2200\n",
      "Epoch 18/100\n",
      "800/800 [==============================] - 1s 2ms/sample - loss: 1.9236 - acc: 0.2700 - val_loss: 1.9146 - val_acc: 0.2800\n",
      "Epoch 19/100\n",
      "800/800 [==============================] - 1s 2ms/sample - loss: 1.8720 - acc: 0.2512 - val_loss: 1.9679 - val_acc: 0.3300\n",
      "Epoch 20/100\n",
      "800/800 [==============================] - 1s 2ms/sample - loss: 1.8882 - acc: 0.2637 - val_loss: 1.9762 - val_acc: 0.2750\n",
      "Epoch 21/100\n",
      "800/800 [==============================] - 1s 2ms/sample - loss: 1.8971 - acc: 0.2725 - val_loss: 1.8750 - val_acc: 0.2850\n",
      "Epoch 22/100\n",
      "800/800 [==============================] - 1s 2ms/sample - loss: 1.7767 - acc: 0.3150 - val_loss: 1.7657 - val_acc: 0.3200\n",
      "Epoch 23/100\n",
      "800/800 [==============================] - 1s 2ms/sample - loss: 1.8313 - acc: 0.2887 - val_loss: 1.7519 - val_acc: 0.3250\n",
      "Epoch 24/100\n",
      "800/800 [==============================] - 1s 2ms/sample - loss: 1.7416 - acc: 0.3338 - val_loss: 1.8607 - val_acc: 0.2700\n",
      "Epoch 25/100\n",
      "800/800 [==============================] - 1s 2ms/sample - loss: 1.7434 - acc: 0.3288 - val_loss: 1.7768 - val_acc: 0.2850\n",
      "Epoch 26/100\n",
      "800/800 [==============================] - 1s 2ms/sample - loss: 1.7675 - acc: 0.3300 - val_loss: 1.7079 - val_acc: 0.3150\n",
      "Epoch 27/100\n",
      "800/800 [==============================] - 1s 2ms/sample - loss: 1.6782 - acc: 0.3475 - val_loss: 1.9383 - val_acc: 0.3350\n",
      "Epoch 28/100\n",
      "800/800 [==============================] - 1s 2ms/sample - loss: 1.6900 - acc: 0.3537 - val_loss: 1.8886 - val_acc: 0.3550\n",
      "Epoch 29/100\n",
      "800/800 [==============================] - 1s 2ms/sample - loss: 1.6740 - acc: 0.3738 - val_loss: 1.8849 - val_acc: 0.3150\n",
      "Epoch 30/100\n",
      "800/800 [==============================] - 1s 2ms/sample - loss: 1.6301 - acc: 0.3800 - val_loss: 1.7493 - val_acc: 0.3150\n",
      "Epoch 31/100\n",
      "800/800 [==============================] - 1s 2ms/sample - loss: 1.6162 - acc: 0.3650 - val_loss: 1.7407 - val_acc: 0.3000\n",
      "Epoch 32/100\n",
      "800/800 [==============================] - 1s 2ms/sample - loss: 1.6049 - acc: 0.3525 - val_loss: 1.8171 - val_acc: 0.3500\n",
      "Epoch 33/100\n",
      "800/800 [==============================] - 1s 2ms/sample - loss: 1.6090 - acc: 0.3512 - val_loss: 1.8719 - val_acc: 0.3200\n",
      "Epoch 34/100\n",
      "800/800 [==============================] - 1s 2ms/sample - loss: 1.5175 - acc: 0.4162 - val_loss: 2.0396 - val_acc: 0.3400\n",
      "Epoch 35/100\n",
      "800/800 [==============================] - 1s 2ms/sample - loss: 1.5767 - acc: 0.3762 - val_loss: 1.8323 - val_acc: 0.3350\n",
      "Epoch 36/100\n",
      "800/800 [==============================] - 1s 2ms/sample - loss: 1.5571 - acc: 0.3950 - val_loss: 1.9488 - val_acc: 0.3200\n",
      "Epoch 37/100\n",
      "800/800 [==============================] - 1s 2ms/sample - loss: 1.5910 - acc: 0.4238 - val_loss: 1.6513 - val_acc: 0.3300\n",
      "Epoch 38/100\n",
      "800/800 [==============================] - 1s 2ms/sample - loss: 1.4351 - acc: 0.4363 - val_loss: 1.6975 - val_acc: 0.3500\n",
      "Epoch 39/100\n",
      "800/800 [==============================] - 1s 2ms/sample - loss: 1.4740 - acc: 0.4175 - val_loss: 1.6046 - val_acc: 0.3500\n",
      "Epoch 40/100\n",
      "800/800 [==============================] - 1s 2ms/sample - loss: 1.4380 - acc: 0.4638 - val_loss: 1.8321 - val_acc: 0.3750\n",
      "Epoch 41/100\n",
      "800/800 [==============================] - 1s 2ms/sample - loss: 1.4949 - acc: 0.4275 - val_loss: 1.7709 - val_acc: 0.3600\n",
      "Epoch 42/100\n",
      "800/800 [==============================] - 1s 2ms/sample - loss: 1.4663 - acc: 0.4450 - val_loss: 1.6126 - val_acc: 0.3550\n",
      "Epoch 43/100\n",
      "800/800 [==============================] - 1s 2ms/sample - loss: 1.3223 - acc: 0.4988 - val_loss: 2.4921 - val_acc: 0.3050\n",
      "Epoch 44/100\n",
      "800/800 [==============================] - 1s 2ms/sample - loss: 1.4017 - acc: 0.4775 - val_loss: 1.7968 - val_acc: 0.3150\n",
      "Epoch 45/100\n",
      "800/800 [==============================] - 1s 2ms/sample - loss: 1.3225 - acc: 0.4900 - val_loss: 1.9591 - val_acc: 0.2950\n",
      "Epoch 46/100\n",
      "800/800 [==============================] - 1s 2ms/sample - loss: 1.3687 - acc: 0.4675 - val_loss: 1.6727 - val_acc: 0.3500\n",
      "Epoch 47/100\n",
      "800/800 [==============================] - 1s 2ms/sample - loss: 1.3289 - acc: 0.4762 - val_loss: 1.7334 - val_acc: 0.4150\n",
      "Epoch 48/100\n",
      "800/800 [==============================] - 1s 2ms/sample - loss: 1.3487 - acc: 0.4638 - val_loss: 1.6102 - val_acc: 0.3600\n",
      "Epoch 49/100\n",
      "800/800 [==============================] - 1s 2ms/sample - loss: 1.2665 - acc: 0.5100 - val_loss: 1.8979 - val_acc: 0.3950\n",
      "Epoch 50/100\n",
      "800/800 [==============================] - 1s 2ms/sample - loss: 1.2901 - acc: 0.5088 - val_loss: 1.8165 - val_acc: 0.3500\n",
      "Epoch 51/100\n",
      "800/800 [==============================] - 1s 2ms/sample - loss: 1.2457 - acc: 0.5088 - val_loss: 1.9050 - val_acc: 0.3550\n",
      "Epoch 52/100\n",
      "800/800 [==============================] - 1s 2ms/sample - loss: 1.2683 - acc: 0.5000 - val_loss: 1.8646 - val_acc: 0.3600\n",
      "Epoch 53/100\n",
      "800/800 [==============================] - 1s 2ms/sample - loss: 1.2379 - acc: 0.5288 - val_loss: 1.8118 - val_acc: 0.3600\n",
      "Epoch 54/100\n",
      "800/800 [==============================] - 1s 2ms/sample - loss: 1.1910 - acc: 0.5238 - val_loss: 1.9699 - val_acc: 0.3650\n",
      "Epoch 55/100\n",
      "800/800 [==============================] - 1s 2ms/sample - loss: 1.2385 - acc: 0.5325 - val_loss: 1.7026 - val_acc: 0.3950\n",
      "Epoch 56/100\n",
      "800/800 [==============================] - 1s 2ms/sample - loss: 1.1191 - acc: 0.5750 - val_loss: 1.9218 - val_acc: 0.3800\n",
      "Epoch 57/100\n",
      "800/800 [==============================] - 1s 2ms/sample - loss: 1.1560 - acc: 0.5663 - val_loss: 1.8377 - val_acc: 0.3600\n",
      "Epoch 58/100\n",
      "800/800 [==============================] - 1s 2ms/sample - loss: 1.0738 - acc: 0.5950 - val_loss: 1.9229 - val_acc: 0.3400\n",
      "Epoch 59/100\n",
      "800/800 [==============================] - 1s 2ms/sample - loss: 1.2203 - acc: 0.5562 - val_loss: 1.7195 - val_acc: 0.4300\n",
      "Epoch 60/100\n",
      "800/800 [==============================] - 1s 2ms/sample - loss: 0.9751 - acc: 0.6463 - val_loss: 2.9766 - val_acc: 0.2500\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 61/100\n",
      "800/800 [==============================] - 1s 2ms/sample - loss: 1.2143 - acc: 0.5587 - val_loss: 2.0174 - val_acc: 0.4000\n",
      "Epoch 62/100\n",
      "800/800 [==============================] - 1s 2ms/sample - loss: 0.9632 - acc: 0.6300 - val_loss: 1.7912 - val_acc: 0.4000\n",
      "Epoch 63/100\n",
      "800/800 [==============================] - 1s 2ms/sample - loss: 1.0180 - acc: 0.6062 - val_loss: 2.0160 - val_acc: 0.3750\n",
      "Epoch 64/100\n",
      "800/800 [==============================] - 1s 2ms/sample - loss: 1.0615 - acc: 0.6162 - val_loss: 1.9116 - val_acc: 0.4000\n",
      "Epoch 65/100\n",
      "800/800 [==============================] - 1s 2ms/sample - loss: 1.0045 - acc: 0.6288 - val_loss: 1.7985 - val_acc: 0.3750\n",
      "Epoch 66/100\n",
      "800/800 [==============================] - 1s 2ms/sample - loss: 1.0411 - acc: 0.6250 - val_loss: 1.8820 - val_acc: 0.4000\n",
      "Epoch 67/100\n",
      "800/800 [==============================] - 1s 2ms/sample - loss: 0.9719 - acc: 0.6475 - val_loss: 2.2907 - val_acc: 0.3500\n",
      "Epoch 68/100\n",
      "800/800 [==============================] - 1s 2ms/sample - loss: 1.0219 - acc: 0.6450 - val_loss: 1.8518 - val_acc: 0.3750\n",
      "Epoch 69/100\n",
      "800/800 [==============================] - 1s 2ms/sample - loss: 0.8565 - acc: 0.6800 - val_loss: 2.1764 - val_acc: 0.3350\n",
      "Epoch 70/100\n",
      "800/800 [==============================] - 1s 2ms/sample - loss: 0.9254 - acc: 0.6612 - val_loss: 2.2117 - val_acc: 0.3800\n",
      "Epoch 71/100\n",
      "800/800 [==============================] - 1s 2ms/sample - loss: 0.8857 - acc: 0.6650 - val_loss: 2.0179 - val_acc: 0.4250\n",
      "Epoch 72/100\n",
      "800/800 [==============================] - 1s 2ms/sample - loss: 0.8440 - acc: 0.6975 - val_loss: 2.0089 - val_acc: 0.4300\n",
      "Epoch 73/100\n",
      "800/800 [==============================] - 1s 2ms/sample - loss: 0.8729 - acc: 0.6812 - val_loss: 2.4063 - val_acc: 0.3850\n",
      "Epoch 74/100\n",
      "800/800 [==============================] - 1s 2ms/sample - loss: 0.8453 - acc: 0.6675 - val_loss: 2.1799 - val_acc: 0.3550\n",
      "Epoch 75/100\n",
      "800/800 [==============================] - 1s 2ms/sample - loss: 0.8145 - acc: 0.7200 - val_loss: 2.8472 - val_acc: 0.3500\n",
      "Epoch 76/100\n",
      "800/800 [==============================] - 1s 2ms/sample - loss: 0.9089 - acc: 0.6737 - val_loss: 2.0278 - val_acc: 0.4300\n",
      "Epoch 77/100\n",
      "800/800 [==============================] - 1s 2ms/sample - loss: 0.7508 - acc: 0.7387 - val_loss: 2.1250 - val_acc: 0.4050\n",
      "Epoch 78/100\n",
      "800/800 [==============================] - 1s 2ms/sample - loss: 0.7485 - acc: 0.7175 - val_loss: 2.1654 - val_acc: 0.3850\n",
      "Epoch 79/100\n",
      "800/800 [==============================] - 1s 2ms/sample - loss: 0.7616 - acc: 0.7212 - val_loss: 2.1831 - val_acc: 0.4450\n",
      "Epoch 80/100\n",
      "800/800 [==============================] - 1s 2ms/sample - loss: 0.7414 - acc: 0.7350 - val_loss: 2.5039 - val_acc: 0.4000\n",
      "Epoch 81/100\n",
      "800/800 [==============================] - 1s 2ms/sample - loss: 0.8105 - acc: 0.6988 - val_loss: 2.8254 - val_acc: 0.3950\n",
      "Epoch 82/100\n",
      "800/800 [==============================] - 1s 2ms/sample - loss: 0.6343 - acc: 0.7788 - val_loss: 2.4664 - val_acc: 0.3850\n",
      "Epoch 83/100\n",
      "800/800 [==============================] - 1s 2ms/sample - loss: 0.6637 - acc: 0.7500 - val_loss: 3.0244 - val_acc: 0.3600\n",
      "Epoch 84/100\n",
      "800/800 [==============================] - 1s 2ms/sample - loss: 0.9063 - acc: 0.7200 - val_loss: 2.2529 - val_acc: 0.4750\n",
      "Epoch 85/100\n",
      "800/800 [==============================] - 1s 2ms/sample - loss: 0.6630 - acc: 0.7700 - val_loss: 2.2222 - val_acc: 0.3500\n",
      "Epoch 86/100\n",
      "800/800 [==============================] - 1s 2ms/sample - loss: 0.7012 - acc: 0.7350 - val_loss: 2.1257 - val_acc: 0.4200\n",
      "Epoch 87/100\n",
      "800/800 [==============================] - 1s 2ms/sample - loss: 0.6165 - acc: 0.7700 - val_loss: 3.0105 - val_acc: 0.3000\n",
      "Epoch 88/100\n",
      "800/800 [==============================] - 1s 2ms/sample - loss: 0.5748 - acc: 0.8150 - val_loss: 3.1052 - val_acc: 0.3150\n",
      "Epoch 89/100\n",
      "800/800 [==============================] - 1s 2ms/sample - loss: 0.6123 - acc: 0.7862 - val_loss: 2.7017 - val_acc: 0.3900\n",
      "Epoch 90/100\n",
      "800/800 [==============================] - 1s 2ms/sample - loss: 0.6244 - acc: 0.7775 - val_loss: 3.2874 - val_acc: 0.3600\n",
      "Epoch 91/100\n",
      "800/800 [==============================] - 1s 2ms/sample - loss: 0.5784 - acc: 0.8062 - val_loss: 2.9908 - val_acc: 0.3950\n",
      "Epoch 92/100\n",
      "800/800 [==============================] - 1s 2ms/sample - loss: 0.7103 - acc: 0.7563 - val_loss: 2.6805 - val_acc: 0.3950\n",
      "Epoch 93/100\n",
      "800/800 [==============================] - 1s 2ms/sample - loss: 0.6157 - acc: 0.7862 - val_loss: 2.6868 - val_acc: 0.4100\n",
      "Epoch 94/100\n",
      "800/800 [==============================] - 1s 2ms/sample - loss: 0.9162 - acc: 0.7763 - val_loss: 2.9325 - val_acc: 0.3800\n",
      "Epoch 95/100\n",
      "800/800 [==============================] - 1s 2ms/sample - loss: 0.4106 - acc: 0.8612 - val_loss: 3.0469 - val_acc: 0.4050\n",
      "Epoch 96/100\n",
      "800/800 [==============================] - 1s 2ms/sample - loss: 0.5420 - acc: 0.8025 - val_loss: 2.5243 - val_acc: 0.4400\n",
      "Epoch 97/100\n",
      "800/800 [==============================] - 1s 2ms/sample - loss: 0.5749 - acc: 0.8075 - val_loss: 3.4701 - val_acc: 0.3800\n",
      "Epoch 98/100\n",
      "800/800 [==============================] - 1s 2ms/sample - loss: 0.3202 - acc: 0.8938 - val_loss: 3.4672 - val_acc: 0.4050\n",
      "Epoch 99/100\n",
      "800/800 [==============================] - 1s 2ms/sample - loss: 1.1238 - acc: 0.7225 - val_loss: 2.6361 - val_acc: 0.4150\n",
      "Epoch 100/100\n",
      "800/800 [==============================] - 1s 2ms/sample - loss: 0.2709 - acc: 0.9175 - val_loss: 3.1415 - val_acc: 0.4100\n"
     ]
    }
   ],
   "source": [
    "#训练模型\n",
    "history=model.fit(x_train[:1000],y_train[:1000],\n",
    "                  batch_size=64,\n",
    "                  epochs=100,\n",
    "                  validation_split=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#损失函数\n",
    "loss=history.history['loss']\n",
    "acc=history.history['acc']\n",
    "val_loss=history.history['val_loss']\n",
    "val_acc=history.history['val_acc']\n",
    "epochs=range(1,len(loss)+1)\n",
    "plt.figure(figsize=(20,5))\n",
    "plt.subplot(1,2,1)\n",
    "plt.plot(epochs,loss,'b')\n",
    "plt.title('Training Loss')\n",
    "plt.xlabel('epochs')\n",
    "plt.subplot(1,2,2)\n",
    "plt.plot(epochs,acc,'r')\n",
    "plt.title('Training Accuracy')\n",
    "plt.xlabel('epochs')\n",
    "plt.show()\n",
    "plt.figure(figsize=(20,5))\n",
    "plt.subplot(1,2,1)\n",
    "plt.plot(epochs,val_loss,'b')\n",
    "plt.title('Validation Loss')\n",
    "plt.xlabel('epochs')\n",
    "plt.subplot(1,2,2)\n",
    "plt.plot(epochs,val_acc,'r')\n",
    "plt.title('Validation Accuracy')\n",
    "plt.xlabel('epochs')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From d:\\users\\administrator\\miniconda3\\envs\\cv\\lib\\site-packages\\tensorflow_core\\python\\ops\\resource_variable_ops.py:1781: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "If using Keras pass *_constraint arguments to layers.\n",
      "INFO:tensorflow:Assets written to: resnet_model\\assets\n"
     ]
    }
   ],
   "source": [
    "#保存模型\n",
    "model.save('resnet_model')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
